Analyze Ewings DR and Expression

R Preliminaries and Data Load/Merge

Load Data

We’ll load the expression data from our lab’s evolution experiment, drug response data for both cell lines, and the drug response data specific to samples that were included in the lab’s sequencing experiments.

The drug response data has been preprocessed as follows:
- For each date, evolutionary replicates 6-8 are controls. The mean EC50 value between these three replicates is extracted for each drug, termed the control_value - For each date, a each replicate is divided by the control_value for each drug, leaving us with the fold change for each replicate:drug combination at each date. - The log2 is taken for each value, in order to put the increased fold changes on the same scale as the decreased fold changes.

Heatmaps of EC50 changes

 [1] "ActD"   "Cyclo"  "Doxo"   "Etp"    "NaThio" "Ola"    "Paz"    "SAHA"  
 [9] "SN38"   "SP"     "TMZ"    "Vin"   
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Figure 2

Supplementary Figure 1

Figure 4

Supplementary Figure 3

Waterfall plots of Drug Response changes

Let’s take a look at some waterfall plots of the drug response for all samples sent from RNA-sequencing. In addition to being shown below, the plots will be saved in the following filepath Plots/Waterfall/RNA_Seq.

waterfall.plot <- function(df, column.name, cell_line, metric, times){
  # This function will create a waterfall style bar plot using the data from a
  # column, column.name in df.
  # The x axis will display the row names of the data frame.
  # The y axis will display the value for each row in the specified column.
  # The x axis will be ordered based on descending y axis value, creating a
  # the waterfall style.
  # Each plot will be saved as a PNG
  # df$labels <- row.names(df)
  df$labels <- times
  # df$times <- times
  df$Treatment_Time <- c("gray75", "gray60", "gray50", "gray15",
                         "gray75", "gray60", "gray50", "gray15",
                         "gray75", "gray60", "gray50", "gray15", "gray70", "gray40",
                         "gray75", "gray60", "gray50", "gray15")
  df <- df %>% arrange(desc(UQ(sym(column.name))))
  # df$labels <- factor(times, levels = times) #Order sample labels in ranked order so plot will have descending waterfall pattern
  df$labels <- factor(df$labels, levels=df$labels)
  max.val <- ceiling(max(df[ , column.name]))
  min.val <- ceiling(min(df[ , column.name]))
  # Create separate directories for each drug's subgroup
  plotDir <- "../Plots"
  dir.create(file.path(plotDir), showWarnings = FALSE)
  plotWater <- "Waterfall"
  dir.create(file.path(plotDir, plotWater), showWarnings = FALSE)
  subsetDir <- "RNA_Seq"
  dir.create(file.path(plotDir, plotWater, subsetDir), showWarnings = FALSE)
  # File name of new plot
  filename <- paste0(column.name, "_", metric, ".png")
  if(metric == "EC50)"){
    metric <- "EC50 (mM)"
  }
  if(metric == "EC50)"){
    metric <- "EC50 (mM)"
  } # Adds unit for only EC50/EC50, because AUC should not have units
  waterfall <- ggplot(df, aes(x=labels, y = UQ(sym(column.name)), fill = UQ(sym(column.name)))) +
          geom_bar(stat="identity") + 
          ggtitle(paste(" Sequenced samples against ", column.name, sep = "")) +
          ylab("log2(\u0394EC50)") +
          xlab("Sample Label") +
    theme_bw() +
          theme(axis.text.x = element_text(angle = 70, vjust = 0.5, hjust=0.5, 
                                           size = 22, color = df$Treatment_Time),
                axis.title.x = element_text(size = 30), 
                axis.title.y = element_text(size = 30),
                plot.title = element_text(size = 40, hjust = 0.5),
                legend.title = element_text(size = 22),
                legend.text = element_text(size = 22),
                legend.title.align=0.5,
                legend.background = element_rect(fill="transparent")) +
          scale_y_discrete(breaks=c(min.val, ceiling(min.val/2), ceiling(min.val/4), 0, 
                                    ceiling(max.val/4), ceiling(max.val/2), max.val)) +
          scale_fill_gradient2(name = "log2(\u0394EC50)",
                               low = "#2066AC", mid = "white", high = "red3")
  png(file.path(plotDir, plotWater, subsetDir, filename), 
      width = 1400, height = 1400, units = "px", pointsize = 50, 
      res = 125)
  print(waterfall)
  dev.off()
}

timepoints <- c("R1-PreTx", "R1-IE1", "R1-VDC3", "R1-IE4",
                "R3-PreTx", "R3-IE1", "R3-VDC3", "R3-IE4",
                "R4-PreTx","R4-VDC1", "R4-IE1", "R4-IE2", "R4-VDC3", "R4-IE4",
                "R5-PreTx", "R5-IE1", "R5-VDC3", "R5-IE4")

for (drug in colnames(EC50_change_data_seq)) { 
                                        # Removes Time column from waterfall plotting
  EC50_change_data_seq["I1", "Vin"] <- NA
  waterfall.plot(df = EC50_change_data_seq, column.name = drug, 
                 cell_line = "A673", metric = "EC50", times = timepoints)
  # Make waterfall plot for change in EC50 for samples sent to sequencing against each drug
  filename <- paste0("../Plots/Waterfall/RNA_Seq/", drug, "_EC50.png")
  cat("![](",filename,")")
}

Point-Range Plots of EC50 over time

plot_point_range <- function(EC50_df, cell_line=c("A673", "A673_Complete", "TTC466", "TTC466_Complete")){
  # EC50_df: data frame where rows contain individual replicates at individual time points
  #          and columns contain drugs
  # Unfortunately for now this function is very specific to the data format that I have
  # I've got a migraine, and that's just how it's gotta be for now
  # Format data
  if(cell_line %in% c("A673", "A673_Complete")){
    if(cell_line=="A673_Complete"){
      cell_line <- "A673 Complete Data"
    }
    EC50_df <- melt(EC50_df)
    EC50_df$rep_group <- rep(c("Experimental", "Experimental", "Experimental", "Experimental", 
                                 "Control", "Control", "Control"), 12)
    EC50_df$time <- factor(c(rep("Pre-Tx", 7),
                        rep("VDC1", 7), 
                        rep("EC1", 7), 
                        rep("VDC2", 7),
                        rep("EC2", 7),
                        rep("VDC3", 7),
                        rep("EC3", 7),
                        rep("VDC4", 7),
                        rep("EC4", 7),
                        rep("VDC5", 9), # This is funky because of some of the cells growing to 
                        rep("EC5", 2), # confluence faster than others
                        rep("VDC5", 3),
                        rep("EC5", 7)), ordered = TRUE, 
                        levels = c("Pre-Tx", "VDC1", "EC1", "VDC2", "EC2", 
                                   "VDC3", "EC3", "VDC4", "EC4", "VDC5", "EC5"))
    if(cell_line=="A673"){
      add_rows <- EC50_df[c(75:77, 159:161, 243:245, 327:329, 411:413,
                            495:497, 579:581, 663:665, 747:749), ]
      add_rows$time <- rep("EC5", nrow(add_rows))
      EC50_df <- rbind(EC50_df, add_rows)
    }
    if(cell_line=="A673 Complete Data"){
      add_rows <- EC50_df[c(75:77, 159:161, 243:245, 327:329, 411:413,
                            495:497, 579:581, 663:665, 747:749, 831:833,
                            915:917, 999:1001), ]
      add_rows$time <- rep("EC5", nrow(add_rows))
      EC50_df <- rbind(EC50_df, add_rows)
    }
  }
  if(cell_line %in% c("TTC466", "TTC466_Complete")){
    if(cell_line=="TTC466_Complete"){
      cell_line <- "TTC466 Complete Data"
    }
    EC50_df["TimeE_R3", "NaThio"] <- NA
    EC50_df["TimeE_R8", "Paz"] <- NA
    EC50_df["TimeA_R7", "Paz"] <- NA
    EC50_df <- melt(EC50_df)
    EC50_df$rep_group <- rep(c("Experimental", "Experimental", "Experimental", 
                                 "Experimental", "Experimental", 
                                 "Control", "Control", "Control"), 11)
    EC50_df$time <- factor(c(rep("Pre-Tx", 8),
                      rep("VDC1", 8), 
                      rep("EC1", 8), 
                      rep("VDC2", 8),
                      rep("EC2", 8),
                      rep("VDC3", 8),
                      rep("EC3", 8),
                      rep("VDC4", 8),
                      rep("EC4", 8),
                      rep("VDC5", 8),
                      rep("EC5", 8)),
                      ordered = TRUE, 
                        levels = c("Pre-Tx", "VDC1", "EC1", "VDC2", "EC2", 
                                   "VDC3", "EC3", "VDC4", "EC4", "VDC5", "EC5"))
  }
  # Order drug variable so that poorly modeled drugs show up as last 3
  EC50_df$variable <- factor(EC50_df$variable, ordered = TRUE, 
                            levels = c("ActD", "Cyclo", "Doxo", "Etp", "Ola", 
                                        "SAHA", "SN38", "SP", "TMZ", "Vin", 
                                        "NaThio", "Paz"))
  EC50_df %>% 
    group_by(variable, rep_group, time) %>%
    summarise(mean_EC50 = mean(value, na.rm = TRUE), 
              max_EC50 = max(value, na.rm = TRUE), 
              min_EC50 = min(value, na.rm = TRUE)) -> EC50_summary
  # Plot figure
  png(filename = paste0("../Plots/Point_Range/pointrange_", cell_line, ".png"),
      width = 1900, height = 1400, res = 200)
  print(ggplot(EC50_summary, aes(x=time, y=mean_EC50, ymin=min_EC50, ymax=max_EC50,
                                 color = factor(rep_group))) +
          geom_pointrange(fatten=2.5) + 
          scale_colour_manual(name = "Experimental Group",
                              values = c("Experimental" = "darkseagreen4", 
                                         "Control" = "burlywood3")) +
          facet_wrap(vars(variable), ncol = 3, scales = "free_y") +
          ggtitle("EC50 values across time points between experimental and control replicates",
                  subtitle = paste(cell_line, "cell line")) +
          labs(x = "Experimental Time Point", y = "EC50 (uM)") +
          theme_bw() +
          theme(
            axis.text.x = element_text(angle = 45, vjust=1, hjust=1),
            plot.title = element_text(hjust = 0.5), 
            plot.subtitle = element_text(hjust = 0.5))) 
  dev.off()
}

EC50_data_A673_edit <- EC50_data_A673_main
EC50_data_A673_edit["TimeJ_R5", "Ola"] <- NA
plot_point_range(EC50_data_A673_edit, cell_line = "A673")
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Figure 2

Supplementary Figure 2

Supplementary Figure 4

Differential Gene Expression Analysis

Here, we’ll build our helper functions for running the differential gene expression analysis.

updown_genes <- function(expr.lst, de.genes){
  ### This function will return two lists of up/down regulated 
  ### genes when a cell line is sensitive to each drug. 
  ### One list will contain genes that 
  ### are up regulated in the lower responding cell lines, 
  ### and one in the lower responding lines.
  
  ### The function will take a matrix (expr.lst$x) containing 
  ### the expression values for the cell lines pertaining 
  ### to the DE expression analysis being run. The cell lines (columns)
  ### correspond to the class labels found in expr.lst$y.
  ### Cell lines labeled '1' have a high AUC to the drug in 
  ### question and cell lines labeled '2' have a low AUC 
  ### to the drug in question.
  
  ### First, the matrix will be separated into high/low AUC
  ### cell lines. Then, a loop will be run with the genes that are 
  ### given as differentially expressed. The genes with a higher
  ### average in the high AUC cell lines will be labeled as genes
  ### whose expression is increased. Genes with a lower average in the
  ### high AUC cell lines will be labeled as genes whose expression is 
  ### decreased. 
  
  data.mat <- expr.lst$x # Expression values
  data.class <- expr.lst$y # Class values
  res.cells_lines <- data.mat[ , which(data.class == 1)] # Resistant, high AUC
  sens.cell_lines <- data.mat[ , which(data.class == 2)] # Sensitive, low AUC
  up.genes <- c() # Up in resistant
  down.genes <- c() # Down in resistant
  extra.genes <- c()
  for(gene in de.genes){
    mean.res <- rowMeans(res.cells_lines[gene, ]) # Mean expr in resistant
    mean.sens <- rowMeans(sens.cell_lines[gene, ]) # Mean expr in sensitive
    if (mean.sens > mean.res) { # Sensitive expr > Resistant expr?
      up.genes <- c(up.genes, gene) # High expr in sens = up-regulated
    } else if (mean.sens < mean.res) { # Sensitive expr < Resistant expr?
      down.genes <- c(down.genes, gene) # Low expr in sens = down-regulated
    } else {
      extra.genes <- c(extra.genes, gene) # 
    }
  }
  return(list(up = up.genes, down = down.genes, extra = extra.genes))
}


high_low_subset <- function(drug_data, drug, percent){
  low_quant <- quantile(drug_data[ , drug], probs = percent, na.rm = TRUE)
  high_quant <- quantile(drug_data[ , drug], probs = 1-percent, na.rm = TRUE)
  median_EC50 <- median(drug_data[ , drug])
  high_lines <- row.names(drug_data[drug_data[ , drug] > high_quant , ])
  low_lines <- row.names(drug_data[drug_data[ , drug] < low_quant , ])
  return(list(high_lines = high_lines, 
              low_lines = low_lines))
}


input_DE <- function(drug_data, expr_data, meta_df, subset_cat = NULL,
                     group_value = NULL, drug, dr_type, cutoff){
  # This is a crucial function for creating the input to the user-defined DE fns
  # below. 
  # First it subsets the data if a subset category and group value are provided.
  # Next, it finds the names of high/low cell lines based on the drug data,
  # speficied drug, and percent of top/bottom responders that we are comparing.
  # Then, it extracts the drug data for the given cell line names and labels them
  # as 1 for high drug response metric or 2 for low drug response metric.
  #
  # A list is return containing the following: 
  #   x: drug response data for the combined high and low cell lines
  #   y: label, 1 or 2, categorizing x's cell lines as high or low responders
  #   num.high: length of high cell lines
  #   num.low: length of low cell lines # probably not necessary, will likely remove later
  # if(is.null(group.value) == FALSE){
  #   drug.data <- subset.df(meta.df = meta.df,
  #                          data.df = drug.data,
  #                          meta.ID.index = 2,
  #                          data.ID.index = "row",
  #                          column = subset.cat,
  #                          group.value = group.value)}
  high_low_lines <- high_low_subset(drug_data, drug, percent = cutoff)
  high_lines <- high_low_lines$high_lines
  low_lines <- high_low_lines$low_lines
  highresp_expr <- expr_data[ , colnames(expr_data) %in% high_lines]
  lowresp_expr <- expr_data[ , colnames(expr_data) %in% low_lines]
  x <- cbind(highresp_expr, lowresp_expr)
  y <- c(rep(2, ncol(highresp_expr)), rep(1, ncol(lowresp_expr)))
  return(list(x = x, y = y, 
              num_high = ncol(highresp_expr), 
              num_low = ncol(lowresp_expr)))
}


updown_genes <- function(expr.lst, de.genes){
  ### This function will return two lists of up/down regulated 
  ### genes when a cell line is sensitive to each drug. 
  ### One list will contain genes that 
  ### are up regulated in the lower responding cell lines, 
  ### and one in the lower responding lines.
  
  ### The function will take a matrix (expr.lst$x) containing 
  ### the expression values for the cell lines pertaining 
  ### to the DE expression analysis being run. The cell lines (columns)
  ### correspond to the class labels found in expr.lst$y.
  ### Cell lines labeled '1' have a high AUC to the drug in 
  ### question and cell lines labeled '2' have a low AUC 
  ### to the drug in question.
  
  ### First, the matrix will be separated into high/low AUC
  ### cell lines. Then, a loop will be run with the genes that are 
  ### given as differentially expressed. The genes with a higher
  ### average in the high AUC cell lines will be labeled as genes
  ### whose expression is increased. Genes with a lower average in the
  ### high AUC cell lines will be labeled as genes whose expression is 
  ### decreased. 
  
  data.mat <- expr.lst$x # Expression values
  data.class <- expr.lst$y # Class values
  res.cells_lines <- data.mat[ , which(data.class == 2)] # Resistant, high AUC
  sens.cell_lines <- data.mat[ , which(data.class == 1)] # Sensitive, low AUC
  up.genes <- c() # Up in resistant
  down.genes <- c() # Down in resistant
  extra.genes <- c()
  for(gene in de.genes){
    mean.res <- rowMeans(res.cells_lines[gene, ]) # Mean expr in resistant
    mean.sens <- rowMeans(sens.cell_lines[gene, ]) # Mean expr in sensitive
    if (mean.res > mean.sens) { # Resistant expr > sensitive expr?
      up.genes <- c(up.genes, gene) # High expr in resistant = up-regulated
    } else if (mean.res < mean.sens) { # Resistant expr < sensitive expr?
      down.genes <- c(down.genes, gene) # Low expr in resistant = down-regulated
    } else {
      extra.genes <- c(extra.genes, gene) # 
    }
  }
  return(list(up = up.genes, down = down.genes, extra = extra.genes))
}


EBSeq_DE <- function(expr_lst){
  data_mat <- as.matrix(expr_lst$x) # Expression values
  data_class <- factor(expr_lst$y) # Class values
  EBOut <- EBTest(Data=data_mat, 
                  Conditions=data_class,
                  sizeFactors=MedianNorm(data_mat), 
                  maxround = 15)
  return(EBOut)
}

run_de_analysis <- function(drug_data, expr_data, drug, group_perc, FDR){
  input_df <- input_DE(drug_data = drug_data,
                       expr_data = expr_data,
                       drug = drug,
                       cutoff = group_perc)
  de_all <- EBSeq_DE(input_df)
  de_signif <- GetDEResults(de_all, FDR = FDR)
  updown_de <- updown_genes(input_df, de_signif$DEfound)
  write.table(updown_de$up, 
              file = paste0("../DE_Results/DE_up_resistant_", drug, ".csv"),
              sep = ",", 
              quote = FALSE, row.names = FALSE, col.names = FALSE)
  write.table(updown_de$down, 
              file = paste0("../DE_Results/DE_up_sensitive_", drug, ".csv"), 
              sep = ",",
              quote = FALSE, row.names = FALSE, col.names = FALSE)
}

And finally, we’ll run our differential expression analysis using EBSeq between the top/bottom 1/3 of responders to each drug.

[1] “ActD” Removing transcripts with 100 th quantile < = 0 20962 transcripts will be tested [1] “Up in resistance:”
V1
1 SNORA24B
2 SNORA27
3 SNORD116-14
[1] “Up in sensitivity:”
V1
1 CLN8
2 DENND4B
3 G6PD
4 IKBKB
5 MEG8
6 NRG1-IT1
7 NSUN5P1
8 PARVG
9 SYNGAP1
10 ZNF582-AS1
[1] “Cyclo” Removing transcripts with 100 th quantile < = 0 20497 transcripts will be tested [1] “Up in resistance:”
V1
1 ALX1
2 AMPD2
3 ANKRD29
4 ANO5
5 ARID1B
6 ATF5
7 BAHCC1
8 BBS1
9 BCR
10 BOLA3
11 BUD31
12 C18orf32
13 CAD
14 CAMTA1
15 CDC42SE2
16 CLCN6
17 COL4A2
18 CYB561
19 DCN
20 DDIT4
21 DDX11
22 DGCR8
23 DHCR7
24 DYNC1H1
25 FABP5P7
26 FAM102B
27 FAM72D
28 FLNA
29 FLRT2
30 G6PD
31 GCN1
32 GNAI1
33 GNG10
34 GTF2A2
35 HDAC10
36 HIST1H1C
37 HIST1H1T
38 HMGN3
39 HOXB7
40 HSPA2
41 HYOU1
42 INTS1
43 KIF1A
44 KIFC3
45 LDLR
46 LENG8
47 LINC01089
48 LITAF
49 LRCH4
50 LSS
51 MAN2C1
52 MTMR4
53 NAA20
54 NDUFA5
55 NEBL
56 NSUN5P1
57 ORMDL3
58 PCDH17
59 PCNX3
60 PCSK9
61 PDCD2L
62 PELP1
63 PIEZO1
64 PLXNA1
65 POLR2K
66 PRPF38B
67 PRRC2A
68 RALGDS
69 RIPPLY3
70 RNF141
71 RPS26P8
72 RTN1
73 RUNDC3B
74 RWDD1
75 SBNO2
76 SERPINH1
77 SGSM2
78 SLC12A7
79 SLC29A2
80 SLC39A8
81 SLC6A9
82 SPTAN1
83 SRRM2
84 SRSF11
85 SSTR1
86 SUCLG2
87 SUPT5H
88 TMEM60
89 TOP3B
90 TRAPPC9
91 TRIM16
92 TUBGCP6
93 VKORC1L1
94 ZNF654
95 ZNF76
[1] “Up in sensitivity:”
V1
1 ADGRL2
2 AHNAK
3 ALK
4 ANAPC2
5 ARHGEF9
6 BACE1-AS
7 C7
8 CAMKV
9 CCN1
10 CD101
11 CD24
12 CD44
13 CLASRP
14 CNOT3
15 COL7A1
16 CRMP1
17 CYFIP2
18 CYP4F23P
19 DENND4B
20 DERL3
21 EFHC1
22 FAM27C
23 FASN
24 GUCY1A2
25 HS6ST2
26 JUN
27 KIAA0556
28 KMT2B
29 LINC01237
30 LRCH2
31 MALAT1
32 MCF2L
33 MED12
34 MEG3
35 MEG8
36 MRGPRF
37 MVD
38 MYH10
39 NBEAL2
40 NEFM
41 NUP210
42 PARVG
43 PC
44 PLEC
45 PYGO1
46 RASAL3
47 RNA5SP284
48 RNF130
49 RNF44
50 SNORA5C
51 SPIN4
52 SYNGAP1
53 TEPSIN
54 TMEM271
55 TSPAN2
56 WDR27
57 ZNF239
58 ZNF667-AS1
59 ZNF736
[1] “Doxo” Removing transcripts with 100 th quantile < = 0 20962 transcripts will be tested [1] “Up in resistance:”
V1
1 LEPROT
2 WDR53
[1] “Up in sensitivity:”
V1
1 ARHGEF9
2 KSR1
3 LAMB3
4 PARVG
[1] “Etp” Removing transcripts with 100 th quantile < = 0 21009 transcripts will be tested [1] “Up in resistance:”
V1
1 ANKRD29
2 COPRS
3 DCN
4 DUT
5 HMGN3
6 LLPH
7 LYN
8 MCTS2P
9 NDUFAF6
10 RPL21P39
11 RUNDC3B
12 SLC39A8
13 SSBP2
14 SSTR1
15 TOGARAM1
16 ZNF654
[1] “Up in sensitivity:”
V1
1 ABCC10
2 ADAM32
3 ARHGEF9
4 CAD
5 CD24
6 CD44
7 CDH4
8 CLASRP
9 CLU
10 CNOT3
11 COL7A1
12 CORO7
13 CREB5
14 DENND4B
15 DHCR7
16 FASN
17 G6PD
18 GUCY1A2
19 HYOU1
20 IKBKB
21 INTS1
22 KATNB1
23 KCNH5
24 KSR1
25 LAMB3
26 LDLR
27 LENG8
28 MAN2C1
29 MCF2L
30 MEG8
31 MINK1
32 MRGPRF
33 MTHFR
34 NEBL
35 NEFM
36 NPDC1
37 NUCB1
38 PAX7
39 PC
40 PCSK7
41 PCSK9
42 PLXNB1
43 PRPF38B
44 PTX3
45 RAD9A
46 RHOD
47 SBNO2
48 SLC7A5
49 SVIL
50 SYNGAP1
51 TMEM94
52 TRAPPC9
53 TSPAN2
54 ZNF354C
55 ZNF582-AS1
56 ZNF736
[1] “NaThio” Removing transcripts with 100 th quantile < = 0 21126 transcripts will be tested [1] “Up in resistance:”
V1
No Differentially Expressed Genes Found 1
[1] “Up in sensitivity:”
V1
1 RN7SL4P
[1] “Ola” Removing transcripts with 100 th quantile < = 0 20941 transcripts will be tested [1] “Up in resistance:”
V1
1 ANO10
2 FAM220A
3 HIST1H1T
4 HMGN3
5 HOXB7
6 PPP1R21
7 SAMD11
8 SNORA27
9 SSTR1
10 TAGLN
[1] “Up in sensitivity:”
V1
1 ADGRL2
2 AHNAK
3 ARHGEF28
4 ATG4B
5 CCL2
6 CCN1
7 CD24
8 CHD6
9 CLASRP
10 CLCN6
11 CLN8
12 DAB2
13 DENND4B
14 DHCR7
15 DYNC1H1
16 ERV3-1
17 FAM27C
18 FASN
19 HSPA2
20 IKBKB
21 JUN
22 KIFC3
23 LARGE2
24 LDLR
25 LENG8
26 LINC02104
27 LMF1
28 LTBP3
29 MAN2C1
30 MCF2L
31 MED15
32 MEG8
33 MGAT5
34 MRGPRF
35 NEFM
36 NRG1-IT1
37 P2RY11
38 PC
39 PCSK7
40 PKN3
41 PLXNB1
42 PTX3
43 SIDT2
44 SLC6A9
45 SRRM2
46 SULT1B1
47 TECPR1
48 TMEM25
49 TMEM94
50 TRIM16L
51 TSPAN2
52 TTLL7
53 UNKL
[1] “Paz” Removing transcripts with 100 th quantile < = 0 21031 transcripts will be tested [1] “Up in resistance:”
V1
1 ZDHHC2
2 ZNF559
[1] “Up in sensitivity:”
V1
No Differentially Expressed Genes Found 1
[1] “SAHA” Removing transcripts with 100 th quantile < = 0 20758 transcripts will be tested [1] “Up in resistance:”
V1
1 ABCB1
2 KAZALD1
3 RPS26
4 SMAD6
5 TRGC1
[1] “Up in sensitivity:”
V1
1 ACOT9
2 ACPP
3 AHR
4 B3GNT5
5 CCL2
6 FOS
7 GAL
8 NUP188
9 RN7SL5P
10 SCNN1G
11 TRAV5
[1] “SN38” Removing transcripts with 100 th quantile < = 0 21089 transcripts will be tested [1] “Up in resistance:”
V1
1 ANKRD29
2 ATP5MF
3 DCN
4 DUT
5 EXOSC3
6 FAM72D
7 HMGB1P10
8 HMGN3
9 LSM8
10 MED21
11 PSMG1
12 RBIS
13 RPL21
14 RUNDC3B
15 SLC39A8
16 SLIRP
17 SMR3B
18 SSBP2
19 SSTR1
20 WDR53
[1] “Up in sensitivity:”
V1
1 ABCC10
2 ADGRL2
3 ALK
4 AP5Z1
5 ARHGEF9
6 CAD
7 CCL2
8 CDH4
9 COL16A1
10 COL7A1
11 CORO7
12 CREB5
13 CRMP1
14 CYB561
15 DENND4B
16 DGCR8
17 DHCR7
18 EBI3
19 ECM1
20 FASN
21 FOS
22 G6PD
23 GPNMB
24 HSPA2
25 HYOU1
26 IKBKB
27 INTS1
28 KATNB1
29 KCNH5
30 LAMB3
31 LDLR
32 LENG8
33 LIPH
34 LOX
35 LUCAT1
36 MAN2C1
37 MAP1S
38 MEG8
39 MRGPRF
40 MTHFR
41 NEBL
42 NEFM
43 NFRKB
44 NLRC5
45 NPDC1
46 NPIPB3
47 NUCB1
48 PAX7
49 PCBP2-OT1
50 PCSK7
51 PCSK9
52 PIP5KL1
53 PLXNB1
54 PNPLA6
55 PRPF38B
56 PTOV1-AS2
57 PTX3
58 RNF44
59 SBNO2
60 SCNN1G
61 SLC7A5
62 SLITRK3
63 SUPT6H
64 SYNGAP1
65 SYT12
66 THY1
67 TRAPPC9
68 USP32P1
69 UTP3
70 ZNF354C
71 ZNF736
[1] “SP” Removing transcripts with 100 th quantile < = 0 20952 transcripts will be tested [1] “Up in resistance:”
V1
1 ADGRL2
2 ANKS6
3 AP3B2
4 AP5Z1
5 ARHGEF9
6 C7
7 CAMKV
8 CCAR2
9 CD24
10 CDH4
11 CHGA
12 CORO7
13 CRMP1
14 DGCR8
15 DHCR7
16 DPP3
17 EPHA4
18 FASN
19 FOXO3B
20 FRG2FP
21 GALNS
22 HBA2
23 HDAC10
24 INCENP
25 INTS1
26 KSR1
27 LIN28B
28 LINC01089
29 LRCH2
30 MAN2C1
31 MEG3
32 MEG8
33 MRGPRF
34 MRNIP
35 MSRA
36 NEB
37 NEFM
38 NOM1
39 NUP210
40 PBX1
41 PC
42 PCBP2-OT1
43 PCDH17
44 PLXNB1
45 PPP1R1B
46 PRRC2B
47 PTPRG-AS1
48 PYGO1
49 RNF130
50 RNF44
51 SBNO2
52 SCAMP4
53 SCARA3
54 SLC16A7
55 SLC29A2
56 SLITRK3
57 SYK
58 TAF15
59 TAF1C
60 TAF6L
61 TMEM271
62 TUBB3
63 VAX1
64 WDR17
65 WDR27
66 ZNF354C
67 ZNF414
68 ZNF667
69 ZNF667-AS1
70 ZNF675
71 ZNF730
72 ZNF736
[1] “Up in sensitivity:”
V1
1 ALX1
2 AMZ2
3 APOBEC3C
4 ARHGEF6
5 CD63
6 DCN
7 FAM72D
8 FAM92A
9 HIST1H1T
10 IL33
11 IRX3
12 LINC00326
13 LITAF
14 LYN
15 MRPS18C
16 NPIPA5
17 NRG1
18 PCSK6
19 PTGR1
20 PYCARD
21 RTN1
22 SP100
23 SSTR1
24 TMEM192
25 TSPAN5
26 YAF2
27 ZFAND1
28 ZNF277
[1] “TMZ” Removing transcripts with 100 th quantile < = 0 21188 transcripts will be tested [1] “Up in resistance:”
V1
1 CBR3
2 NRGN
3 RUNDC3B
4 TRIR
[1] “Up in sensitivity:”
V1
1 ADAM32
2 CCL2
3 LDLR
4 LINC02104
5 LUCAT1
6 NPIPB3
7 PRPF38B
8 RBM25
9 SNHG20
10 TPR
11 UTP3
[1] “Vin” Removing transcripts with 100 th quantile < = 0 20760 transcripts will be tested [1] “Up in resistance:”
V1
1 FAM220A
[1] “Up in sensitivity:”
V1
1 CALD1
2 CTSL
3 DNAJB9
4 FLT1
5 FSTL1
6 S100A13
7 TRNP1

Jessica Scarborough

2020-02-14